Rolling early warning method and system of dynamic security risk situation for large scale hybrid ac/dc grids

Abstract

A rolling early warning method and system of dynamic security risk situation for large scale hybrid alternating current/direct current grids, which includes: constructing the original feature set of the fast total transfer capability (TTC) estimation model; generating the training sample set of the fast TTC estimation model based on the network topology and the forecast information of a period of time in the future; constructing the fast TTC estimation model based on stacking denoising autoencoders and extreme learning machine; generating the future operating conditions (OCs), and determine the type of preventive control actions needed to ensure the system security based on the fast TTC estimation model and the heuristic search algorithm; and conducting the layered and hierarchical early warning for OCs according to the type of operating state and the type of preventive control actions that is needed.

1 . A rolling early warning method of dynamic security risk situation for large scale hybrid alternating current/direct current (AC/DC) grids, the method comprising:
 construct a fast total transfer capability (TTC) estimation model based on stacking denoising autoencoders (SDAE) and extreme learning machine (ELM); train the fast TTC estimation model by training samples;   generate future operating conditions (OCs) based on forecast information of load power and renewable generation;   determine the type of preventive control actions needed to satisfy an available transfer capability (ATC) margin constraint by combining the fast TTC estimation model and a heuristic search algorithm;   according to type of operating state and type of preventive control actions that is needed, perform layered and hierarchical early warning for future OCs;   periodically acquire latest load power forecasts, latest renewable generation forecasts and latest preventive control resource information; update early warning results and achieve rolling early warning.   
     
     
         2 . The method of  claim 1 , wherein, a process of generating the training sample set is:
 load power fluctuation intervals, wind power fluctuation intervals and network topology of a period of time in the future are acquired;   unlabeled samples are generated; load power and wind power are randomly varied in the fluctuation intervals; generator power is calculated by predefined dispatching principles; preventive control variables are randomly varied in predefined variation intervals;   TTC values of the unlabeled samples are calculated; continuation power flow is used to verify static security constraints, and time-domain simulation is used to verify dynamic security constraints.   
     
     
         3 . The method of  claim 1 , wherein, an original feature set of the fast TTC estimation model is constructed by considering operating features relevant to TTC and control variables of preventive control actions; the original feature set includes active power of loads, generators, wind farms and high-voltage direct-current (HVDC) links, amount of power of generator power re-dispatch, HVDC set-point control and load shedding. 
     
     
         4 . The method of  claim 1 , wherein, in the fast TTC estimation model, SDAE is used to extract high-level representations from original input features; ELM is utilized to construct nonlinear mapping relationship between the high-level representations and TTC; model output is TTC value of a given OC considering all contingencies. 
     
     
         5 . The method of  claim 1 , wherein, a training process of the fast TTC estimation model is:
 each hidden layer of SDAE is decoupled and constructed as a denoising autoencoder (DAE); DAEs are trained one by one by unsupervised learning with all training samples;   after SDAE is trained, high-level representations extracted by SDAE are taken as new sample features; ELM is trained by supervised learning with all training samples.   
     
     
         6 . The method of  claim 1 , wherein, ATC margin is defined as ratio of ATC to existing transfer commitment (ETC); the ATC margin constraint that needs to be satisfied is 
       
         
           
             
               
                 
                   
                     
                       
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       where m is a predefined minimum margin value. 
     
     
         7 . The method of  claim 1 , wherein, a process of determining the type of preventive control actions needed to satisfy the ATC margin constraint is:
 control sensitivities of every control variable to the ATC margin are approximately calculated; first, a certain control variable is changed with a small increment; then, variation of TTC is calculated by the TTC estimation model, and variation of the ATC margin is calculated;   all control variables, whose control sensitivities exceed predefined sensitivity thresholds, are changed a step toward direction of improving the ATC margin;   control variables are checked to judge whether to exceed control limits; if a control variable exceeds predefined control limit, the control variable is set as corresponding upper or lower limit;   loop termination conditions are checked, if one of termination conditions is satisfied, the search process is terminated.   
     
     
         8 . The method of  claim 1 , wherein, a process of rolling early warning is:
 after forecasts of load power and renewable generation are periodically updated, acquire latest forecast information and latest preventive control resource information;   generate a future OC set for early warning based on the latest forecast information;   determine first-layer early warning results and second-layer early warning results for future OCs that are generated;   periodically update the previous early warning results; if the forecast information will be periodically updated again, wait for the next update; otherwise, the process of the rolling early warning is stopped.   
     
     
         9 . The method of  claim 1 , wherein, a process of layered and hierarchical early warning is:
 first-layer early warning ranking is performed according to type of operating state of a power system; in increasing order of insecurity, a ranking strategy includes:   no warning, level I, level II, and level III;   after the first-layer early warning ranking is finished, if the power system is not in a normal and secure state, second-layer early warning ranking is conducted, which is based on type of preventive control actions needed to satisfy the ATC margin constraint; a ranking strategy is:   level 1: intra-area generator power re-dispatch is needed;   level 2: inter-area HVDC set-point control is needed;   level 3: load shedding is needed;   level 4: ATC margin constraint cannot be satisfied even by a best combination of available preventive control actions.   
     
     
         10 . A rolling early warning system of dynamic security risk situation for large scale hybrid AC/DC grids, the system comprising:
 a module of fast TTC estimation model construction, which is used to construct the fast TTC estimation model based on SDAE and ELM, and train the fast TTC estimation model by training samples;   a module of OC set generation, which is used to generate future OCs based on load forecasts and renewable generation forecasts;   a module of control cost calculation, which is used to determine the type of preventive control actions needed to satisfy the ATC margin constraint by combining the fast TTC estimation model and a heuristic search algorithm;   a module of early warning ranking, which is used to perform the layered and hierarchical early warning for future OCs according to the type of operating state and the type of preventive control actions that is needed;   a module of rolling update of early warning results, which is used to periodically acquire latest load power forecasts, latest renewable generation forecasts and latest preventive control resource information, update early warning results, and achieve rolling early warning.